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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal INFOTEL Semantik Techno.Com: Jurnal Teknologi Informasi Proceeding of the Electrical Engineering Computer Science and Informatics Fountain of Informatics Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal on Software Engineering (IJSE) Faktor Exacta Jukung (Jurnal Teknik Lingkungan) CogITo Smart Journal JRMSI - Jurnal Riset Manajemen Sains Indonesia KACANEGARA Jurnal Pengabdian pada Masyarakat Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Informatik : Jurnal Ilmu Komputer Jurnal Riset Informatika jurnal teknik informatika dan sistem informasi Scientific Journal of Informatics Idealis : Indonesia Journal Information System SKANIKA: Sistem Komputer dan Teknik Informatika Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Ticom: Technology of Information and Communication Journal Of Communication Education Telematika MKOM
Achmad Solichin
Universitas Budi Luhur
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Social Sciences Other

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Rekomendasi Pemilihan Mata Kuliah dalam Pengisian Rencana Studi Mahasiswa dengan Penerapan Algoritma Apriori Syahrul, Ahmad; Solichin, Achmad
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol 6 No 1 (2022)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v6i1.522

Abstract

Pada masa pengisian rencana studi, banyak mahasiswa Universitas Budi Luhur merasa kesulitan dalam memilih mata kuliah yang akan diambil di semester berikutnya. Kesalahan dalam pemilihan mata kuliah dapat berdampak pada pencapaian prestasi yang tidak optimal serta dapat menghambat kelulusan maha-siswa. Peran dosen pembimbing akademik (PA) dalam membantu mahasiswa menentukan mata kuliah di semester yang akan datang sangatlah penting, tetapi pada beberapa kasus, terdapat dosen pembimbing akademik yang tidak memiliki waktu untuk berdiskusi secara langsung dengan mahasiswa. Oleh karena itu, penelitian ini bertujuan mengembangkan sebuah sistem yang dapat memberikan rekomendasi bagi mahasiswa dalam pengambilan mata kuliah pada saat mengisi rencana studi untuk semester selanjutnya. Sistem rekomendasi pemilihan mata kuliah ini menggunakan metode association rule mining dengan al-goritma apriori. Data riwayat pengambilan mata kuliah mahasiswa dievaluasi menggunakan algoritma apriori untuk selanjutnya dihasilkan rekomendasi pengambilan mata kuliah di semester berikutnya. Hasil pengujian terhadap sistem menunjukkan rata-rata persentase akurasi rekomendasi rencana studi sebesar 80,16% dengan minimum confidence sebesar 80%, dan minimum support sebanyak 7. Hasil tersebut menunjukkan bahwa sistem ini dapat membantu mahasiswa dalam melakukan pemilihan mata kuliah di semester yang akan datang.
PEMODELAN ARSITEKTUR TEKNOLOGI INFORMASI BERBASIS CLOUD COMPUTING UNTUK INSTITUSI PERGURUAN TINGGI DI INDONESIA Achmad Solichin; Zainal A. Hasibuan
Semantik Vol 2, No 1 (2012): Prosiding Semantik 2012
Publisher : Semantik

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (780.871 KB)

Abstract

Teknologi cloud computing merupakan teknologi dimana sebagian besar proses dan komputasi terletak di jaringan internet sehingga memungkinkan pengguna dapat mengakses layanan yang diperlukan dari manapun dan kapan pun. Menurut datadari Direktorat Jenderal Pendidikan Tinggi (DIKTI), saat ini di Indonesia terdapat lebih dari 3.000 perguruan tinggi baik swasta maupun negeri. Secara umum, seluruh perguruan tinggi tersebut memiliki kesamaan proses bisnis dan kebutuhan sistem informasi. Namun dalam kenyataannya setiap perguruan tinggi membangun arsitektur dan kebutuhan sistem informasinya secara sendiri-sendiri. Hal tersebut menimbulkan beberapa permasalahan yaitu terjadi redundancy sistem informasi, tidak ada standardisasi struktur data, tidak terjadi konsolidasi data antara sistem informasi yang satu dengan yang lainnya dan inconsistency pengembangan sistem informasi. Beberapa permasalahan tersebut menjadi dasar pemikiran untuk merancang suatu model arsitektur teknologi informasi menggunakan konsep cloud computing.Berdasarkan hasil analisis terhadap kondisi perguruan tinggi di Indonesia dan menggunakan framework yang telah dipilih, dihasilkan suatu model arsitektur teknologi informasi berbasis cloud computing yang sesuai dengan kondisi dan kebutuhan perguruan tinggi di Indonesia. Arsitektur teknologi informasi tersebut terdiri dari arsitektur bisnis, arsitekturdata, arsitektur aplikasi dan arsitektur teknologi. Penelitian diharapkan dapat memberikan kontribusi bagi perguruan tinggi di Indonesia terutama sebagai acuan dalam merancang arsitektur teknologi informasi.Kata kunci : arsitektur teknologi informasi, cloud computing, perguruan tinggi di indonesia
Pengamanan M-Commerce Menggunakan One Time Password Metode Pseudo Random Number Generator (PRNG) Muhammad Fahrizal; Achmad Solichin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 5 No 2 (2020): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (991.897 KB) | DOI: 10.36341/rabit.v5i2.1363

Abstract

Mobile commerce or m-commerce is an electronic trading system (e-commerce) that uses mobile equipment such as mobile phones, smart phones, PDAs, and notebooks. With the growth of smartphone users throughout the world, many electronic commerce business owners also provide m-commerce applications to make it easier for their customers to make transactions. In addition to providing convenience for users, m-commerce application providers must be able to ensure that customers can transact safely. Security risks are one of the major obstacles in the development of electronic commerce systems. Therefore, this research applies the method of securing m-commerce applications using one time password (OTP) generated by the Pseudo Random Number Generator (PRNG) method. This study also modified the PRNG algorithm by doing three bit shifting processes and adding encryption algorithms. The test results show that the system can generate an OTP that is always unique for each transaction. The results of this study are useful for m-commerce application developers to secure their applications.
Penerapan Metode Haversine Pada Sistem Informasi Geografis Untuk Penentuan Lokasi Pembangunan Menara Telekomunikasi Pada Kota Tangerang Achmad Maulana; Achmad Solichin; Mohammad Syafrullah
Indonesian Journal on Software Engineering (IJSE) Vol 4, No 1 (2018): IJSE 2018
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (596.353 KB) | DOI: 10.31294/ijse.v4i1.6294

Abstract

Abstract - Development Telecommunication Tower growth cannot be avoided. Accordingly, the local government makes local regulations on Planning and Control of Telecommunication Tower to avoid illegal construction of Telecommunication Tower because makes a reduction in open land and also reduces the aesthetic value of the spatial region. The researcher conducted a study on the implementation of methods haversine on a geographic information system is expected to simplify the process of distance measurements for the determination of Telecommunication Tower towards he elliptical shape of the earth. Keywords: GIS, haversine, RAD, google map
Metode Background Subtraction untuk Deteksi Obyek Pejalan Kaki pada Lingkungan Statis Achmad Solichin; Agus Harjoko
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2013
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Penelitian ini mencoba menerapkan teknik pengurangan latar belakang (background subtraction) untuk melakukan segmentasi obyek pada citra. Citra digital diperoleh dari perekaman dengan menggunakan kamera genggam pada suatu area yang sering dilalui oleh pejalan kaki di dalam gedung. Obyek pejalan kaki dipisahkan dari citra latar belakang dengan melakukan pengurangan citra sederhana pada area RGB dan grayscale. Keberadaan noise, bayangan dan ghost dapat dihilangkan dengan proses penebalan (dilatasi) pada citra. Klasifikasi sederhana dilakukan berdasarkan bentuk dari obyek. Penelitian ini dapat mendeteksi obyek pejalan kaki dengan cukup baik hanya dengan menggunakan teknik seleksi berdasarkan ukuran dari obyek.
Optimization of LZW Compression Algorithm With Modification of Dictionary Formation Restu Maulunida; Achmad Solichin
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 1 (2018): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.28707

Abstract

At present, the need to access the data have been transformed into digital data, and its use has been growing very rapidly. This transformation is due to the use of the Internet is growing very rapidly, and also the development of mobile devices are growing massively. People tend to store a lot of files in their storage and transfer files from one media to another media. When approaching the limit of storage media, the fewer files that can be stored. A compression technique is required to reduce the size of a file. The dictionary coding technique is one of the lossless compression techniques, LZW is an algorithm for applying coding dictionary compression techniques. In the LZW algorithm, the process of forming a dictionary uses a future based dictionary and encoding process using the Fixed Length Code. It allows the encoding process to produce a sequence that is still quite long. This study will modify the process of forming a dictionary and use Variable Length Code, to optimize the compression ratio. Based on the test using the data used in this study, the average compression ratio for LZW algorithm is 42,85%, and our proposed algorithm is 38,35%. It proves that the modification of the formation of the dictionary we proposed has not been able to improve the compression ratio of the LZW algorithm.
Comparison of Decision Tree, Naïve Bayes and KNearest Neighbors for Predicting Thesis Graduation Achmad Solichin
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1991

Abstract

Thesis is one of the evaluations of learning for students. In Universitas Budi Luhur (UBL), especially in the Informatics Department, the thesis is one of the requirements for graduating students to obtain a Bachelor of Computer degree. In each semester, the number of Informatics Department students who take thesis is around 200-300 students. The problem that is still faced is that student graduation in the thesis is not optimal. Student failures in the thesis are allegedly related to several technical and nontechnical factors. In this study, an analysis using data mining algorithms was carried out to determine the factors that influence student graduation in the thesis. The dataset obtained from the Informatics Department students who took a thesis in the 2016/2017, and 2017/2018. In order to obtain the right classification method, this research was tested with three classification methods, namely Decision Tree, Naïve Bayes, and k-Nearest Neighbors (kNN). The results of the comparison of the values of accuracy, precision, and recall indicate that the kNN algorithm has advantages, so this method is chosen to predict graduation. In this study also developed an application for predicting graduation of students' thesis by applying the kNN classification method. The test results showed an accuracy of 78.20%, precision of 80.32%, and recall of 96.49%. This research is expected to be useful for improving the service quality of student thesis
Klasterisasi Persebaran Virus Corona (Covid-19) Di DKI Jakarta Menggunakan Metode K-Means Achmad Solichin; Khansa Khairunnisa
Fountain of Informatics Journal Vol 5, No 2 (2020): November
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v5i2.4905

Abstract

AbstrakCorona virus (COVID-19) merupakan jenis virus baru yang ditemukan pada manusia di propinsi Wuhan, Cina pada bulan Desember 2019. Virus ini dapat menular dari manusia ke manusia melalui tetesan kecil (droplet) dari hidung atau mulut pada saat batuk, bersin, atau berbicara. Oleh karena itu, di masa pandemi ini sangat penting untuk menjaga jarak dengan orang lain dan menghindari wilayah dengan persebaran COVID-19 yang tinggi. Pada penelitian ini dilakukan klasterisasi persebaran virus Corona di DKI Jakarta dengan menerapkan metode data mining. Pengelompokan dilakukan berdasarkan parameter jumlah ODP, PDP, kasus Positif, pasien sembuh dan pasien meninggal. Pada penelitian ini, untuk melakukan klasterisasi data digunakan metode K-Means dan metode pengukuran jarak Euclidean. Penelitian ini menghasilkan prototipe aplikasi pengelompokan data persebaran pasien Covid-19. Berdasarkan pengujian, jumlah klaster yang direkomendasikan adalah 9 klaster. Hasil penelitian ini diharapkan dapat membantu pemerintah DKI Jakarta dalam mengambil keputusan strategis dalam mengurangi persebaran virus Corona di DKI Jakarta.Kata kunci: corona, Jakarta, klasterisasi, k-means Abstract[Corona Virus (Covid-19) Clustering in Jakarta using K-Means Method] Coronavirus (COVID-19) is a new type of virus found in humans in the province of Wuhan, China in December 2019. This virus can be transmitted from person to person through small droplets from the nose or mouth when coughing, sneezing, or talking. Therefore, during this pandemic, it is very important to keep your distance from other people and avoid areas with a high spread of COVID-19 In this study, the distribution of the Coronavirus in DKI Jakarta was clustered by applying the data mining method. The clustering was carried out based on the parameters of the number of ODP, PDP, positive cases, patients recovered and patients died. In this study, to perform data clustering, the K-Means method, and the Euclidean distance measurement method were used. This study produced a prototype application for the distribution of Covid-19 patient distribution data. Based on the test, the recommended number of clusters is 9 clusters. The results of this study are expected to help the DKI Jakarta government in making strategic decisions in reducing the spread of the Coronavirus in DKI Jakarta.Keywords: corona, Jakarta, clustering, k-means
Penerapan Metode Naïve Bayes Classifier Untuk Penjurusan Siswa Pada Madrasah Aliyah Al-Falah Jakarta Ahmad Zainul Mafakhir; Achmad Solichin
Fountain of Informatics Journal Vol 5, No 1 (2020): Mei
Publisher : Universitas Darussalam Gontor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21111/fij.v5i1.4007

Abstract

AbstrakPenjurusan siswa di suatu sekolah merupakan suatu proses penempatan siswa ke dalam jurusan tertentu sesuai dengan kemampuan dan keinginan siswa. Madrasah Aliyah Al-Falah (MA Al-Falah) merupakan sekolah yang sederajat dengan Sekolah Menengah Atas (SMA) dan memiliki tiga jurusan yaitu Ilmu-ilmu Keagamaan (IIK), Matematika dan Ilmu Alam (MIA), dan Ilmu-ilmu Sosial (IIS). Saat ini proses penjurusan di MA Al-Falah dilakukan dengan melakukan empat jenis tes yaitu matematika, bahasa Indonesia, bahasa Inggris, dan keagamaan terhadap siswa. Hasil tes tersebut dijadikan dasar penentuan jurusan siswa. Permasalahan yang dihadapi oleh pihak sekolah adalah kesulitan dalam mengklasifikasikan siswa berdasarkan hasil tes penjurusan tersebut. Saat ini teknik penjurusan yang dilakukan sekolah hanya mengandalkan pengolahan data dan pengurutan dengan Microsoft Excel. Pada penelitian ini, dilakukan penerapan metode Naïve Bayes Classifier untuk melakukan klasifikasi nilai tes dan menghasilkan rekomendasi jurusan siswa. Sistem klasifikasi penjurusan siswa yang dikembangkan dapat membantu proses penjurusan siswa dengan lebih mudah, cepat, dan akurat. Berdasarkan pengujian yang telah dilakukan sistem penjurusan siswa dapat memberikan rekomendasi jurusan siswa dengan nilai akurasi sebesar 33,34%.Kata kunci: penjurusan siswa, naïve bayes, sistem klasifikasi Abstract[The Implementation of Naïve Bayes Classifier Method for Student Majorization at Madrasah Aliyah Al-Falah Jakarta] Student majorization in a school is a process of placing students into certain majors according to their abilities and desires. Madrasah Aliyah Al-Falah (MA Al-Falah) is a school equivalent to High School and has three majors namely Religious Sciences (IIK), Mathematics and Natural Sciences (MIA), and Social Sciences (IIS). Currently the majors at MA Al-Falah are carried out by carrying out four types of tests namely mathematics, Indonesian, English, and religion on students. The test results are used as the basis for determining student majors. The problem faced by the school is the difficulty in classifying students based on the results of the majors test. Currently the majors that schools use rely solely on data processing and sequencing with Microsoft Excel. In this study, the Naïve Bayes Classifier method was implemented to classify test scores and produce recommendations for student majors. The student majors classification system developed can help students' majors process more easily, quickly, and accurately. Based on the testing that has been done the student majors system can provide student majors recommendations with an accuracy value of 33,34%.Keywords: student majors, naïve bayes, classification system
Rekomendasi Pemilihan Mata Kuliah Dalam Pengisian Rencana Studi Mahasiswa dengan Penerapan Algoritma Apriori Ahmad Syahrul; Achmad Solichin
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 6 No. 1 (2022)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v6i1.549

Abstract

During the time of filling out the study plan, many Budi Luhur University students find it difficult to choose the courses to be taken in the next semester. Errors in the selection of courses can have an impact on the achievement of achievement that is not optimal, and can inhibit student graduation. The role of academic supervisors in helping students determine the taking of courses in the coming semester is very important, but in some cases, there are academic supervisors who do not have time to discuss directly with students. Therefore, in this study a system is proposed that can provide recommendations for students in taking courses when filling out study plans for the next semester. This course of recommendation making system uses the association rule mining method with a priori algorithm. The student's course history data is evaluated using an a priori algorithm to produce recommendations for choosing courses in the next semester. The trial results of this system show an average percentage of successful recommendations of 80.16% with a minimum confidence of 80%, and a minimum support of 7. With these results, this system can be used as a means of students to help make decisions in making courses at the coming semester.
Co-Authors Abdullah 'Alim Achmad Maulana Agus Harjoko Agus Santoso Ahmad Ihsanudin Ahmad Syahrul Ahmad Zainul Mafakhir Alfredo Pasaribu Andi Jumardi Anggi Ayu Ningtyas Arif, Andi Hakim Bayu Raditya Nasution Bernadeta Asri Rejeki Tulodo Dwi Kristanto Emil Salim Erno Kurniawan Dewantara Galih Gumilar Widhasmara Goenawan Brotosaputro Irennada Ismail Adi Susanto Joko Christian Chandra Kafi Kurnia Akbar Khaeri Diniari Khansa Khairunnisa Lia Amellia Putri M Amiruddin Saddam Mochammad Andika Putra Mohammad Afif Hanafi Mohammad Syafrullah Muhamad Refaldi Muhammad Agus Arianto Muhammad Arif Kurniawan Muhammad Fahrizal Muhammad Hamdi Sukriyandi Muhammad Verdiansyah Nazori AZ Noor Ferdyansyah Obby Oktafianto Painem, Painem Pradana, Rizky Ratna Kusumawardani Reka Dwi Syaputra Restu Maulunida Reva Ragam Santika Riki Wijaya Suherman Achmad Syahrul, Ahmad Triyono, Gandung Ummu Habibah Romlah Wahyu Desena Zainal A. Hasibuan